Optimizing the Energy Consumption of Spiking Neural Networks for Neuromorphic Applications [PDF]
Frontiers in Neuroscience, 2020 In the last few years, spiking neural networks (SNNs) have been demonstrated to perform on par with regular convolutional neural networks. Several works have proposed methods to convert a pre-trained CNN to a Spiking CNN without a significant sacrifice ...
Martino Sorbaro +4 more
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Spiking Neural Networks and Their Applications: A Review
Brain Sciences, 2022 The past decade has witnessed the great success of deep neural networks in various domains. However, deep neural networks are very resource-intensive in terms of energy consumption, data requirements, and high computational costs.
Kashu Yamazaki +3 more
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Exploring the Connection Between Binary and Spiking Neural Networks
Frontiers in Neuroscience, 2020 On-chip edge intelligence has necessitated the exploration of algorithmic techniques to reduce the compute requirements of current machine learning frameworks.
Sen Lu, Abhronil Sengupta
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Efficient event-based delay learning in spiking neural networks [PDF]
Nature CommunicationsSpiking Neural Networks compute using sparse communication and are attracting increased attention as a more energy-efficient alternative to traditional Artificial Neural Networks.
Balázs Mészáros +2 more
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Financial time series prediction using spiking neural networks. [PDF]
PLoS ONE, 2014 In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction.
David Reid +2 more
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Advancing EEG based stress detection using spiking neural networks and convolutional spiking neural networks [PDF]
Scientific ReportsAccurate and efficient analysis of Electroencephalogram (EEG) signals is crucial for applications like neurological diagnosis and Brain-Computer Interfaces (BCI).
Aaditya Joshi +4 more
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BIASNN: a biologically inspired attention mechanism in spiking neural networks for image classification [PDF]
Scientific ReportsSpiking Neural Networks (SNNs), designed to more accurately model the brain’s neurobiological processes, have been proposed as energy-efficient alternatives to conventional Artificial Neural Networks (ANNs), which typically incur high computational and ...
Kevin Takala +2 more
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Spiking Neural Network Model for Brain-like Computing and Progress of Its Learning Algorithm [PDF]
Jisuanji kexue, 2023 With the increasingly prominent limitations of deep neural networks in practical applications,brain-like computing spiking neural networks with biological interpretability have become the focus of research.The uncertainty and complex diversity of ...
HUANG Zenan, LIU Xiaojie, ZHAO Chenhui, DENG Yabin, GUO Donghui
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Molecular Toxicity Virtual Screening Applying a Quantized Computational SNN-Based Framework
Molecules, 2023 Spiking neural networks are biologically inspired machine learning algorithms attracting researchers’ attention for their applicability to alternative energy-efficient hardware other than traditional computers.
Mauro Nascimben, Lia Rimondini
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Event-based backpropagation can compute exact gradients for spiking neural networks
Scientific Reports, 2021 Spiking neural networks combine analog computation with event-based communication using discrete spikes. While the impressive advances of deep learning are enabled by training non-spiking artificial neural networks using the backpropagation algorithm ...
Timo C. Wunderlich, Christian Pehle
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